---
license: mit
task_categories:
- tabular-regression
- tabular-classification
tags:
- nigeria
- oil-and-gas
- energy
- petroleum
- synthetic
language:
- en
size_categories:
- n<1K
data_type: synthetic
---

> ⚠️ **Synthetic dataset** — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference.

# Nigerian Oilgas Reserves

## Dataset Description

This dataset is part of the **Nigerian Oil & Gas Sector** collection, containing comprehensive data on Nigeria's petroleum industry from 1999-2025.

- **Rows**: 27
- **Columns**: 10
- **Period**: 1999-2025 (where applicable)
- **License**: MIT

## Data Quality

- **1999-2014**: ⭐⭐⭐⭐⭐ Based on official NEITI data
- **2015-2025**: ⭐⭐⭐⭐ Validated synthetic (OPEC/NNPC sources)

## Schema

| Column | Type | Sample |
|--------|------|--------|
| `year` | int64 | 1999 |
| `oil_proven_reserves_mmbbl` | int64 | 36602 |
| `oil_probable_reserves_mmbbl` | float64 | 10980.6 |
| `oil_possible_reserves_mmbbl` | float64 | 7320.400000000001 |
| `oil_total_reserves_mmbbl` | float64 | 54903.0 |
| `gas_proven_reserves_tcf` | float64 | 211.9654298686023 |
| `gas_probable_reserves_tcf` | float64 | 52.99135746715058 |
| `gas_possible_reserves_tcf` | float64 | 31.794814480290345 |
| `reserves_replacement_ratio` | float64 | 0.8733739159464655 |
| `data_source` | object | NUPRC/OPEC estimates |

## Usage

```python
from datasets import load_dataset

dataset = load_dataset("electricsheepafrica/nigerian_oilgas_reserves")
df = dataset['train'].to_pandas()
```

## Citation

```bibtex
@dataset{nigerian_oilgas_2025,
  title = {Nigerian Oil & Gas Sector Datasets},
  author = {Electric Sheep Africa},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/electricsheepafrica/nigerian_oilgas_reserves}
}
```

## Collection

Part of: [Nigeria Oil & Gas Sector](https://huggingface.co/collections/electricsheepafrica/nigeria-oil-gas-sector)